






"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
snake_case : Optional[int]     		=		logging.getLogger()

snake_case : Union[str, Any]     		=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def 				a__			(		self :	Tuple				,    UpperCamelCase_ :	Union[str, Any] ):





           '''simple docstring'''

           os.makedirs(UpperCamelCase_				,    exist_ok=UpperCamelCase_ )
           __magic_name__      				=    {'source': 'What is love ?', 'target': 'life'}
           __magic_name__      				=    {'train': 1_2, 'val': 2, 'test': 2}
           for split in ["train", "test", "val"]:
                  for field in ["source", "target"]:
                         __magic_name__      				=    '\n'.join([contents[field]] * n_lines[split] )
                         with open(os.path.join(UpperCamelCase_				,    f"""{split}.{field}""" )				,    'w' ) as f:
                                f.write(UpperCamelCase_ )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	str = "pytorch" ):





           '''simple docstring'''

           __magic_name__      				=    self.get_auto_remove_tmp_dir()
           __magic_name__      				=    os.path.join(UpperCamelCase_				,    'output' )
           __magic_name__      				=    os.path.join(UpperCamelCase_				,    'data' )
           self._create_dummy_data(data_dir=UpperCamelCase_ )

           __magic_name__      				=    f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

           if gpus > 0:
                  testargs.append(f"""--gpus={gpus}""" )
                  if is_apex_available():
                         testargs.append('--fp16' )
           else:
                  testargs.append('--gpus=0' )
                  testargs.append('--distributed_backend=ddp_cpu' )
                  testargs.append('--num_processes=2' )

           __magic_name__      				=    [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
           execute_subprocess_async(UpperCamelCase_				,    env=self.get_env() )

           __magic_name__      				=    os.path.join(UpperCamelCase_				,    'metrics.json' )
           with open(UpperCamelCase_ ) as f:
                  __magic_name__      				=    json.load(UpperCamelCase_ )
           return result







    @require_torch_gpu
    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self._run_finetune(gpus=1 )
           self.assertGreaterEqual(result['test'][0]['test_avg_em']				,    0.2 )







    @require_torch_multi_gpu
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    self._run_finetune(gpus=2 )
           self.assertGreaterEqual(result['test'][0]['test_avg_em']				,    0.2 )







    @require_torch_gpu
    @require_ray
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self._run_finetune(gpus=1				,    distributed_retriever='ray' )
           self.assertGreaterEqual(result['test'][0]['test_avg_em']				,    0.2 )







    @require_torch_multi_gpu
    @require_ray
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    self._run_finetune(gpus=1				,    distributed_retriever='ray' )
           self.assertGreaterEqual(result['test'][0]['test_avg_em']				,    0.2 )







"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)